A Computational Method of Forecasting Based on Fuzzy Time Series with Comparison of Various Mean

Abstract This research article suggests a computational method for constructing fuzzy sets in absence of expert knowledge. This method uses concepts of central tendencies mean and variance. This study gives a solution to the critical issue in designing of fuzzy systems, number of fuzzy sets. Proposed computational method helps in finding intervals and thereby fuzzy sets for fuzzy time series forecasting. Proposed computational method is implemented on the authentic data for the enrolments of University of Alabama, which is considered as benchmark problem in the field of fuzzy time series. The forecasted values are compared with the results of other methods to state its supremacy. Projected computational method along with Gaussian membership function gave promising results over other methods for fuzzy time series for the above said benchmark data.

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A COMPUTATIONAL METHOD FOR FUZZY TIME SERIES FORECASTING BASED ON DIFFERENCE PARAMETERS

International Journal of Modeling Simulation and Scientific Computing ◽

10.1142/s1793962312500237 ◽

2012 ◽

Vol 04 (01) ◽

pp. 1250023 ◽

Cited By ~ 17

Author(s):

BHAGAWATI P. JOSHI ◽

SANJAY KUMAR

Keyword(s):

Time Series ◽

Computational Algorithm ◽

Stock Exchange ◽

Time Series Forecasting ◽

Computational Method ◽

Fuzzy Time Series ◽

University Of Alabama ◽

Market Prices ◽

Current State

Present study proposes a method for fuzzy time series forecasting based on difference parameters. The developed method has been presented in a form of simple computational algorithm. It utilizes various difference parameters being implemented on current state for forecasting the next state values to accommodate the possible vagueness in the data in an efficient way. The developed model has been simulated on the historical student enrollments data of University of Alabama and the obtained forecasted values have been compared with the existing methods to show its superiority. Further, the developed model has also been implemented in forecasting the movement of market prices of share of State Bank of India (SBI) at Bombay Stock Exchange (BSE), India.

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A computational method of forecasting based on high-order fuzzy time series

Expert Systems with Applications ◽

10.1016/j.eswa.2009.02.061 ◽

2009 ◽

Vol 36 (7) ◽

pp. 10551-10559 ◽

Cited By ~ 46

Author(s):

Shiva Raj Singh

Keyword(s):

Time Series ◽

High Order ◽

Computational Method ◽

Fuzzy Time Series

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Fuzzy Time Series Model Based on Intuitionistic Fuzzy Sets for Empirical Research in Stock Market

International Journal of Applied Evolutionary Computation ◽

10.4018/jaec.2012100105 ◽

2012 ◽

Vol 3 (4) ◽

pp. 71-84 ◽

Cited By ~ 22

Author(s):

Bhagawati P. Joshi ◽

Sanjay Kumar

Keyword(s):

Time Series ◽

Fuzzy Sets ◽

Stock Exchange ◽

Intuitionistic Fuzzy Set ◽

Intuitionistic Fuzzy Sets ◽

Computational Approach ◽

Computational Method ◽

Fuzzy Time Series ◽

Market Prices ◽

Intuitionistic Fuzzy

Intuitionistic fuzzy sets introduced by Atanassov are generalization of fuzzy sets as they also handle the non-determinacy which is caused by degree of hesitation of decision maker. The present study proposes a computational method of forecasting for fuzzy time series. In the proposed method the notion of intuitionistic fuzzy set is used in fuzzy time series forecasting with simplified computational approach. The developed model has been tested on the movement of share market prices of State Bank of India (SBI) at Bombay Stock Exchange (BSE), India. Further the method has been implemented for forecasting SENSEX of BSE. The suitability of the developed model has also been examined by comparing it with the other existing models to show its superiority.

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Fuzzy Time Series Forecasting Model Based on Automatic Clustering Techniques and Generalized Fuzzy Logical Relationship

Mathematical Problems in Engineering ◽

10.1155/2015/962597 ◽

2015 ◽

Vol 2015 ◽

pp. 1-8 ◽

Cited By ~ 2

Author(s):

Wangren Qiu ◽

Ping Zhang ◽

Yanhong Wang

Keyword(s):

Time Series ◽

Stock Exchange ◽

Composite Index ◽

High Order ◽

Computational Method ◽

Fuzzy Time Series ◽

Time Series Models ◽

Logical Relationship ◽

Automatic Clustering ◽

Fuzzy Logical

In view of techniques for constructing high-order fuzzy time series models, there are three types which are based on advanced algorithms, computational method, and grouping the fuzzy logical relationships. The last type of models is easy to be understood by the decision maker who does not know anything about fuzzy set theory or advanced algorithms. To deal with forecasting problems, this paper presented novel high-order fuzz time series models denoted as GTS(M, N)based on generalized fuzzy logical relationships and automatic clustering. This paper issued the concept of generalized fuzzy logical relationship and an operation for combining the generalized relationships. Then, the procedure of the proposed model was implemented on forecasting enrollment data at the University of Alabama. To show the considerable outperforming results, the proposed approach was also applied to forecasting the Shanghai Stock Exchange Composite Index. Finally, the effects of parametersMandN, the number of order, and concerned principal fuzzy logical relationships, on the forecasting results were also discussed.

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